Cross-frequency synchronization connects networks of fast and slow oscillations during visual working memory maintenance

Does slow oscillation-spindle coupling contribute to sleep-dependent memory consolidation? A Bayesian meta-analysis

The active system consolidation theory suggests that information transfer between the hippocampus and cortex during sleep underlies memory consolidation. Neural oscillations during sleep, including the temporal coupling between slow oscillations (SO) and sleep spindles (SP), may play a mechanistic role in memory consolidation. However, differences in analytical approaches and the presence of physiological and behavioral moderators have led to inconsistent conclusions. This meta-analysis, comprising 23 studies and 297 effect sizes, focused on four standard phase-amplitude coupling measures including coupling phase, strength, percentage, and SP amplitude, and their relationship with memory retention. We developed a standardized approach to incorporate non-normal circular-linear correlations. We found strong evidence supporting that precise and strong SO-fast SP coupling in the frontal lobe predicts memory consolidation. The strength of this association is mediated by memory type, aging, and dynamic spatio-temporal features, including SP frequency and cortical topography. In conclusion, SO-SP coupling should be considered as a general physiological mechanism for memory consolidation.

Distinct Hippocampal Oscillation Dynamics in Trace Eyeblink Conditioning Task for Retrieval and Consolidation of Associations

Trace eyeblink conditioning (TEBC) has been widely used to study associative learning in both animals and humans. In this paradigm, conditioned responses (CRs) to conditioned stimuli (CS) serve as a measure for retrieving learned associations between the CS and the unconditioned stimuli (US) within a trial. Memory consolidation, that is, learning over time, can be quantified as an increase in the proportion of CRs across training sessions. However, how hippocampal oscillations differentiate between successful memory retrieval within a session and consolidation across TEBC training sessions remains unknown. To address this question, we recorded local field potentials (LFPs) from the rat dorsal hippocampus during TEBC and investigated hippocampal oscillation dynamics associated with these two functions. We show that transient broadband responses to the CS were correlated with memory consolidation, as indexed by an increase in CRs across TEBC sessions. In contrast, induced alpha (8-10 Hz) and beta (16-20 Hz) band responses were correlated with the successful retrieval of the CS-US association within a session, as indexed by the difference in trials with and without CR.

Strengthening of alpha synchronization is a neural correlate of cognitive transfer

Cognitive training can lead to improvements in both task-specific strategies and general capacities, such as visuo-spatial working memory (VSWM). The latter emerge slowly and linearly throughout training, in contrast to strategy where changes typically occur within the first days of training. Changes in strategy and capacity have not been separated in prior neuroimaging studies. Here, we used a within-participants design with dense temporal sampling to capture the time dynamics of neural mechanisms associated with change in capacity. In four participants, neural activity was recorded with magnetoencephalography on seven occasions over two months of visuo-spatial working memory training. During scanning, the participants performed a trained visuo-spatial working memory task, a transfer task, and a control task. First, we extracted an individual visuo-spatial working memory-load-dependent synchronization network for each participant. Next, we identified linear changes over time in the network, congruent with the temporal dynamics of capacity change. Three out of four participants showed a gradual strengthening of alpha synchronization. Strengthening of the same connections was also found in the transfer task but not in the control task. This suggests that cognitive transfer occurs through slow, gradual strengthening of alpha synchronization between cortical regions that are vital for both the trained task and the transfer task.

Cross-frequency and inter-regional phase synchronization in explicit transitive inference

Explicit logical reasoning, like transitive inference, is a hallmark of human intelligence. This study investigated cortical oscillations and their interactions in transitive inference with EEG. Participants viewed premises describing abstract relations among items. They accurately recalled the relationship between old pairs of items, effectively inferred the relationship between new pairs of items, and discriminated between true and false relationships for new pairs. First, theta (4-7 Hz) and alpha oscillations (8-15 Hz) had distinct functional roles. Frontal theta oscillations distinguished between new and old pairs, reflecting the inference of new information. Parietal alpha oscillations changed with serial position and symbolic distance of the pairs, representing the underlying relational structure. Frontal alpha oscillations distinguished between true and false pairs, linking the new information with the underlying relational structure. Second, theta and alpha oscillations interacted through cross-frequency and inter-regional phase synchronization. Frontal theta-alpha 1:2 phase locking appeared to coordinate spectrally diverse neural activity, enhanced for new versus old pairs and true versus false pairs. Alpha-band frontal-parietal phase coherence appeared to coordinate anatomically distributed neural activity, enhanced for new versus old pairs and false versus true pairs. It suggests that cross-frequency and inter-regional phase synchronization among theta and alpha oscillations supports human transitive inference.

Multiplexity of human brain oscillations as a personal brain signature

Human individuality is likely underpinned by the constitution of functional brain networks that ensure consistency of each person's cognitive and behavioral profile. These functional networks should, in principle, be detectable by noninvasive neurophysiology. We use a method that enables the detection of dominant frequencies of the interaction between every pair of brain areas at every temporal segment of the recording period, the dominant coupling modes (DoCM). We apply this method to brain oscillations, measured with magnetoencephalography (MEG) at rest in two independent datasets, and show that the spatiotemporal evolution of DoCMs constitutes an individualized brain fingerprint. Based on this successful fingerprinting we suggest that DoCMs are important targets for the investigation of neural correlates of individual psychological parameters and can provide mechanistic insight into the underlying neurophysiological processes, as well as their disturbance in brain diseases.

Neonatal cortical activity organizes into transient network states that are affected by vigilance states and brain injury

Early neurodevelopment is critically dependent on the structure and dynamics of spontaneous neuronal activity; however, the natural organization of newborn cortical networks is poorly understood. Recent adult studies suggest that spontaneous cortical activity exhibits discrete network states with physiological correlates. Here, we studied newborn cortical activity during sleep using hidden Markov modeling to determine the presence of such discrete neonatal cortical states (NCS) in 107 newborn infants, with 47 of them presenting with a perinatal brain injury. Our results show that neonatal cortical activity organizes into four discrete NCSs that are present in both cardinal sleep states of a newborn infant, active and quiet sleep, respectively. These NCSs exhibit state-specific spectral and functional network characteristics. The sleep states exhibit different NCS dynamics, with quiet sleep presenting higher fronto-temporal activity and a stronger brain-wide neuronal coupling. Brain injury was associated with prolonged lifetimes of the transient NCSs, suggesting lowered dynamics, or flexibility, in the cortical networks. Taken together, the findings suggest that spontaneously occurring transient network states are already present at birth, with significant physiological and pathological correlates; this NCS analysis framework can be fully automatized, and it holds promise for offering an objective, global level measure of early brain function for benchmarking neurodevelopmental or clinical research.

The association between working memory precision and the nonlinear dynamics of frontal and parieto-occipital EEG activity

Electrophysiological working memory (WM) research shows brain areas communicate via macroscopic oscillations across frequency bands, generating nonlinear amplitude modulation (AM) in the signal. Traditionally, AM is expressed as the coupling strength between the signal and a prespecified modulator at a lower frequency. Therefore, the idea of AM and coupling cannot be studied separately. In this study, 33 participants completed a color recall task while their brain activity was recorded through EEG. The AM of the EEG data was extracted using the Holo-Hilbert spectral analysis (HHSA), an adaptive method based on the Hilbert-Huang transforms. The results showed that WM load modulated parieto-occipital alpha/beta power suppression. Furthermore, individuals with higher frontal theta power and lower parieto-occipital alpha/beta power exhibited superior WM precision. In addition, the AM of parieto-occipital alpha/beta power predicted WM precision after presenting a target-defining probe array. The phase-amplitude coupling (PAC) between the frontal theta phase and parieto-occipital alpha/beta AM increased with WM load while processing incoming stimuli, but the PAC itself did not predict the subsequent recall performance. These results suggest frontal and parieto-occipital regions communicate through theta-alpha/beta PAC. However, the overall recall precision depends on the alpha/beta AM following the onset of the retro cue.

Universal Lifespan Trajectories of Source-Space Information Flow Extracted from Resting-State MEG Data

Source activity was extracted from resting-state magnetoencephalography data of 103 subjects aged 18-60 years. The directionality of information flow was computed from the regional time courses using delay symbolic transfer entropy and phase entropy. The analysis yielded a dynamic source connectivity profile, disentangling the direction, strength, and time delay of the underlying causal interactions, producing independent time delays for cross-frequency amplitude-to-amplitude and phase-to-phase coupling. The computation of the dominant intrinsic coupling mode (DoCM) allowed me to estimate the probability distribution of the DoCM independently of phase and amplitude. The results support earlier observations of a posterior-to-anterior information flow for phase dynamics in {α1, α2, β, γ} and an opposite flow (anterior to posterior) in θ. Amplitude dynamics reveal posterior-to-anterior information flow in {α1, α2, γ}, a sensory-motor β-oriented pattern, and an anterior-to-posterior pattern in {δ, θ}. The DoCM between intra- and cross-frequency couplings (CFC) are reported here for the first time and independently for amplitude and phase; in both domains {δ, θ, α1}, frequencies are the main contributors to DoCM. Finally, a novel brain age index (BAI) is introduced, defined as the ratio of the probability distribution of inter- over intra-frequency couplings. This ratio shows a universal age trajectory: a rapid rise from the end of adolescence, reaching a peak in adulthood, and declining slowly thereafter. The universal pattern is seen in the BAI of each frequency studied and for both amplitude and phase domains. No such universal age dependence was previously reported.

Structural connectome constrained graphical lasso for MEG partial coherence

Structural connectivity provides the backbone for communication between neural populations. Since axonal transmission occurs on a millisecond time scale, measures of M/EEG functional connectivity sensitive to phase synchronization, such as coherence, are expected to reflect structural connectivity. We develop a model of MEG functional connectivity whose edges are constrained by the structural connectome. The edge strengths are defined by partial coherence, a measure of conditional dependence. We build a new method-the adaptive graphical lasso (AGL)-to fit the partial coherence to perform inference on the hypothesis that the structural connectome is reflected in MEG functional connectivity. In simulations, we demonstrate that the structural connectivity's influence on the partial coherence can be inferred using the AGL. Further, we show that fitting the partial coherence is superior to alternative methods at recovering the structural connectome, even after the source localization estimates required to map MEG from sensors to the cortex. Finally, we show how partial coherence can be used to explore how distinct parts of the structural connectome contribute to MEG functional connectivity in different frequency bands. Partial coherence offers better estimates of the strength of direct functional connections and consequently a potentially better estimate of network structure.

Treatment effects on event-related EEG potentials and oscillations in Alzheimer's disease

Alzheimer's disease dementia (ADD) is the most diffuse neurodegenerative disorder belonging to mild cognitive impairment (MCI) and dementia in old persons. This disease is provoked by an abnormal accumulation of amyloid-beta and tauopathy proteins in the brain. Very recently, the first disease-modifying drug has been licensed with reserve (i.e., Aducanumab). Therefore, there is a need to identify and use biomarkers probing the neurophysiological underpinnings of human cognitive functions to test the clinical efficacy of that drug. In this regard, event-related electroencephalographic potentials (ERPs) and oscillations (EROs) are promising candidates. Here, an Expert Panel from the Electrophysiology Professional Interest Area of the Alzheimer's Association and Global Brain Consortium reviewed the field literature on the effects of the most used symptomatic drug against ADD (i.e., Acetylcholinesterase inhibitors) on ERPs and EROs in ADD patients with MCI and dementia at the group level. The most convincing results were found in ADD patients. In those patients, Acetylcholinesterase inhibitors partially normalized ERP P300 peak latency and amplitude in oddball paradigms using visual stimuli. In these same paradigms, those drugs partially normalize ERO phase-locking at the theta band (4-7 Hz) and spectral coherence between electrode pairs at the gamma (around 40 Hz) band. These results are of great interest and may motivate multicentric, double-blind, randomized, and placebo-controlled clinical trials in MCI and ADD patients for final cross-validation.

Detection of Cross-Frequency Coupling Between Brain Areas: An Extension of Phase Linearity Measurement

The current paper proposes a method to estimate phase to phase cross-frequency coupling between brain areas, applied to broadband signals, without any a priori hypothesis about the frequency of the synchronized components. N:m synchronization is the only form of cross-frequency synchronization that allows the exchange of information at the time resolution of the faster signal, hence likely to play a fundamental role in large-scale coordination of brain activity. The proposed method, named cross-frequency phase linearity measurement (CF-PLM), builds and expands upon the phase linearity measurement, an iso-frequency connectivity metrics previously published by our group. The main idea lies in using the shape of the interferometric spectrum of the two analyzed signals in order to estimate the strength of cross-frequency coupling. We first provide a theoretical explanation of the metrics. Then, we test the proposed metric on simulated data from coupled oscillators synchronized in iso- and cross-frequency (using both Rössler and Kuramoto oscillator models), and subsequently apply it on real data from brain activity. Results show that the method is useful to estimate n:m synchronization, based solely on the phase of the signals (independently of the amplitude), and no a-priori hypothesis is available about the expected frequencies.

Theta oscillations shift towards optimal frequency for cognitive control

Cognitive control allows to flexibly guide behaviour in a complex and ever-changing environment. It is supported by theta band (4-7 Hz) neural oscillations that coordinate distant neural populations. However, little is known about the precise neural mechanisms permitting such flexible control. Most research has focused on theta amplitude, showing that it increases when control is needed, but a second essential aspect of theta oscillations, their peak frequency, has mostly been overlooked. Here, using computational modelling and behavioural and electrophysiological recordings, in three independent datasets, we show that theta oscillations adaptively shift towards optimal frequency depending on task demands. We provide evidence that theta frequency balances reliable set-up of task representation and gating of task-relevant sensory and motor information and that this frequency shift predicts behavioural performance. Our study presents a mechanism supporting flexible control and calls for a reevaluation of the mechanistic role of theta oscillations in adaptive behaviour.

Processing time affects sequential memory performance beginning at the level of visual encoding

Electrophysiological studies have demonstrated that theta-band activity is useful for investigating neural mechanisms of memory. However, mechanisms specifically driving memory performance remain poorly understood. In sequential memory, performance can be artificially attenuated by shortening the inter-stimulus interval (ISI) between memory item presentations. Therefore, we sought to clarify the mechanisms of sequential memory performance by analyzing theta-band (4–8 Hz) activity recorded via magnetoencephalogram in 33 participants during performance of a sequential memory task where memory items were presented at either slow or fast rates in accordance with longer or shorter ISIs, respectively. Particularly in the slow task, theta activity clearly modulated in accordance with the presentation of memory items. Common cortical target regions in the occipital and frontal cortex were identified in both tasks and related to visual encoding and memory maintenance, respectively. Compared to the slow task, occipital-theta activity was significantly lower in the fast task from the midterm until the ending of encoding, in correspondence with significantly lower recall for memory items in this same period. Meanwhile, despite a loss of clarity in responsiveness to individual memory items in the fast task, frontal-theta activity was not different between tasks and exhibited particularly strong responses in both tasks during the holding period prior to recall. Our results indicate that shorter processing time erodes sequential memory performance beginning at the level of visual encoding.

Dependence of Working Memory on Coordinated Activity Across Brain Areas

Neural signatures of working memory (WM) have been reported in numerous brain areas, suggesting a distributed neural substrate for memory maintenance. In the current manuscript we provide an updated review of the literature focusing on intracranial neurophysiological recordings during WM in primates. Such signatures of WM include changes in firing rate or local oscillatory power within an area, along with measures of coordinated activity between areas based on synchronization between oscillations. In comparing the ability of various neural signatures in any brain area to predict behavioral performance, we observe that synchrony between areas is more frequently and robustly correlated with WM performance than any of the within-area neural signatures. We further review the evidence for alteration of inter-areal synchrony in brain disorders, consistent with an important role for such synchrony during behavior. Additionally, results of causal studies indicate that manipulating synchrony across areas is especially effective at influencing WM task performance. Each of these lines of research supports the critical role of inter-areal synchrony in WM. Finally, we propose a framework for interactions between prefrontal and sensory areas during WM, incorporating a range of experimental findings and offering an explanation for the observed link between intra-areal measures and WM performance.

Working Memory and Cross-Frequency Coupling of Neuronal Oscillations

Working memory (WM) is the active retention and processing of information over a few seconds and is considered an essential component of cognitive function. The reduced WM capacity is a common feature in many diseases, such as schizophrenia, attention deficit hyperactivity disorder (ADHD), mild cognitive impairment (MCI), and Alzheimer's disease (AD). The theta-gamma neural code is an essential component of memory representations in the multi-item WM. A large body of studies have examined the association between cross-frequency coupling (CFC) across the cerebral cortices and WM performance; electrophysiological data together with the behavioral results showed the associations between CFC and WM performance. The oscillatory entrainment (sensory, non-invasive electrical/magnetic, and invasive electrical) remains the key method to investigate the causal relationship between CFC and WM. The frequency-tuned non-invasive brain stimulation is a promising way to improve WM performance in healthy and non-healthy patients with cognitive impairment. The WM performance is sensitive to the phase and rhythm of externally applied stimulations. CFC-transcranial-alternating current stimulation (CFC-tACS) is a recent approach in neuroscience that could alter cognitive outcomes. The studies that investigated (1) the association between CFC and WM and (2) the brain stimulation protocols that enhanced WM through modulating CFC by the means of the non-invasive brain stimulation techniques have been included in this review. In principle, this review can guide the researchers to identify the most prominent form of CFC associated with WM processing (e.g., theta/gamma phase-amplitude coupling), and to define the previously published studies that manipulate endogenous CFC externally to improve WM. This in turn will pave the path for future studies aimed at investigating the CFC-tACS effect on WM. The CFC-tACS protocols need to be thoroughly studied before they can be considered as therapeutic tools in patients with WM deficits.

Phase-Based Cortical Synchrony Is Affected by Prematurity

Inter-areal synchronization by phase–phase correlations (PPCs) of cortical oscillations mediates many higher neurocognitive functions, which are often affected by prematurity, a globally prominent neurodevelopmental risk factor. Here, we used electroencephalography to examine brain-wide cortical PPC networks at term-equivalent age, comparing human infants after early prematurity to a cohort of healthy controls. We found that prematurity affected these networks in a sleep state-specific manner, and the differences between groups were also frequency-selective, involving brain-wide connections. The strength of synchronization in these networks was predictive of clinical outcomes in the preterm infants. These findings show that prematurity affects PPC networks in a clinically significant manner, suggesting early functional biomarkers of later neurodevelopmental compromise that may be used in clinical or translational studies after early neonatal adversity.

Working memory load modulates oscillatory activity and the distribution of fast frequencies across frontal theta phase during working memory maintenance

Working memory (WM) is a keystone of our cognitive abilities. Increasing load has been shown to dampen its performance and affect oscillatory neural activity in different frequency bands. Nevertheless, mixed results regarding fast frequencies activity and a lack of research on WM load modulations of cross-frequency phase-amplitude coupling mechanisms preclude a better understanding of the impact of increased WM load levels on brain activity as well as inter-regional communication and coordination supporting WM processes. Hence, we analyzed the EEG activity of 25 participants while performing a delayed-matching-to-sample (DMS) WM task with three WM load levels. Current density power and distribution at the source level for theta, beta, and gamma frequencies during the task's delay period were compared for each pair of WM load conditions. Results showed maximal increases of theta activity in frontal areas and of fast frequencies' activity in posterior regions with WM load, showing the involvement of frontal theta activity in WM maintenance and the control of attentional resources and visual processing by beta and gamma activity. To study whether WM load modulates communication between cortical areas, posterior beta and gamma amplitudes distribution across frontal theta phase was also analysed for those areas showing the largest significant WM load modulations. Higher beta activity amplitude at bilateral cuneus and right middle occipital gyrus, and higher gamma activity amplitude at bilateral posterior cingulate were observed during frontal theta phase peak in low than high memory load conditions. Moreover, greater fast beta amplitude at the right postcentral gyrus was observed during theta phase trough at right middle frontal gyrus in high than low memory load conditions. These results show that WM load modulates whether interregional communication occurs during theoretically optimal or non-optimal time windows, depending on the demands of frontal control of posterior areas required to perform the task successfully.

Shared resources between visual attention and visual working memory are allocated through rhythmic sampling

Attention and visual working memory (VWM) are among the most theoretically detailed and empirically tested constructs in human cognition. Nevertheless, the nature of the interrelation between selective attention and VWM still presents a fundamental controversy: Do they rely on the same cognitive resources or not? The present study aims at disentangling this issue by capitalizing on recent evidence showing that attention is a rhythmic phenomenon, oscillating over short time windows. Using a dual-task approach, we combined a classic VWM task with a visual detection task in which we densely sampled detection performance during the time between the memory and the test array. Our results show that an increment in VWM load was related to reduced detection of near-threshold visual stimuli. Importantly, we observed an oscillatory pattern in detection at ~7.5 Hz in the low VWM load conditions, which decreased towards ~5 Hz in the high VWM load condition. These findings suggest that the frequency of this sampling rhythm changes according to the allocation of attentional resources to either the VWM or the detection task. This pattern of results is consistent with a central sampling attentional rhythm which allocates shared attentional resources both to the flow of external visual stimulation and to the internal maintenance of visual information.

Decoding working memory task condition using magnetoencephalography source level long-range phase coupling patterns

OBJECTIVE

The objective of the study is to identify phase coupling patterns that are shared across subjects via a machine learning approach that utilises source space magnetoencephalography (MEG) phase coupling data from a working memory (WM) task. Indeed, phase coupling of neural oscillations is putatively a key factor for communication between distant brain areas and is therefore crucial in performing cognitive tasks, including WM. Previous studies investigating phase coupling during cognitive tasks have often focused on a few a priori selected brain areas or a specific frequency band, and the need for data-driven approaches has been recognised. Machine learning techniques have emerged as valuable tools for the analysis of neuroimaging data since they catch fine-grained differences in the multivariate signal distribution. Here, we expect that these techniques applied to MEG phase couplings can reveal WM-related processes that are shared across individuals.

APPROACH

We analysed WM data collected as part of the Human Connectome Project. The MEG data were collected while subjects (n = 83) performed N-back WM tasks in two different conditions, namely 2-back (WM condition) and 0-back (control condition). We estimated phase coupling patterns (multivariate phase slope index) for both conditions and for theta, alpha, beta, and gamma bands. The obtained phase coupling data were then used to train a linear support vector machine in order to classify which task condition the subject was performing with an across-subject cross-validation approach. The classification was performed separately based on the data from individual frequency bands and with all bands combined (multiband). Finally, we evaluated the relative importance of the different features (phase couplings) for classification by the means of feature selection probability.

MAIN RESULTS

The WM condition and control condition were successfully classified based on the phase coupling patterns in the theta (62% accuracy) and alpha bands (60% accuracy) separately. Importantly, the multiband classification showed that phase coupling patterns not only in the theta and alpha but also in the gamma bands are related to WM processing, as testified by improvement in classification performance (71%).

SIGNIFICANCE

Our study successfully decoded WM tasks using MEG source space functional connectivity. Our approach, combining across-subject classification and a multidimensional metric recently developed by our group, is able to detect patterns of connectivity that are shared across individuals. In other words, the results are generalisable to new individuals and allow meaningful interpretation of task-relevant phase coupling patterns.

Frontoparietal Beta Amplitude Modulation and its Interareal Cross-frequency Coupling in Visual Working Memory

Visual working memory (VWM) relies on sustained neural activities that code information via various oscillatory frequencies. Previous studies, however, have emphasized time-frequency power changes, while overlooking the possibility that rhythmic amplitude variations can also code frequency-specific VWM information in a completely different dimension. Here, we employed the recently-developed Holo-Hilbert spectral analysis to characterize such nonlinear amplitude modulation(s) (AM) underlying VWM in the frontoparietal systems. We found that the strength of AM in mid-frontal beta and gamma oscillations during late VWM maintenance and VWM retrieval correlated with people's VWM performance. When behavioral performance was altered with transcranial electric stimulation, AM power changes during late VWM maintenance in beta, but not gamma, tracked participants' VWM variations. This beta AM likely codes information by varying its amplitude in theta period for long-range propagation, as our connectivity analysis revealed that interareal theta-beta couplings-bidirectional between mid-frontal and right-parietal during VWM maintenance and unidirectional from right-parietal to left-middle-occipital during late VWM maintenance and retrieval-underpins VWM performance and individual differences.

EEG alpha-theta dynamics during mind wandering in the context of breath focus meditation: An experience sampling approach with novice meditation practitioners

Meditation practice entails moments of distraction dominated by self-generated thoughts (i.e. mind wandering). Initial studies assessing the neural correlates of mind wandering in the context of meditation practice have identified an important role of theta (4-8 Hz) and alpha (8-14 Hz) neural oscillations. In this study, we use a probe-caught experience sampling paradigm to assess spectral changes in the theta-alpha frequency range during mind wandering in the context of breath focus meditation. Electroencephalography (EEG) was measured in 25 novice meditation practitioners during a breath focus task in which they were repeatedly probed to report whether they were focusing on their breath or thinking about something else. Mind wandering episodes were associated with an increase in the amplitude and a decrease in the frequency of theta (4-8 Hz) oscillations. Conversely, alpha oscillations (8-14 Hz) were shown to decrease in amplitude and increase in frequency during mind wandering relative to breath focus. In addition, mind wandering episodes were shown to be accompanied by increased harmonicity and phase synchrony between alpha and theta rhythms. Because similar spectral changes in the theta-alpha frequency range have been reported during controlled cognitive processes involving memory and executive control, we speculate that mind wandering and controlled processes could share some neurocognitive mechanisms. From a translational perspective, this study indicates that oscillatory activity in the theta-alpha frequency range could form adequate parameters for developing EEG-neurofeedback protocols aimed at facilitating the detection of mind wandering during meditation practice.

Revealing a multiplex brain network through the analysis of recurrences

A multilayer approach has recently received particular attention in network neuroscience as a suitable model to describe brain dynamics by adjusting its activity in different frequency bands, time scales, modalities, or ages to different layers of a multiplex graph. In this paper, we demonstrate an approach to a frequency-based multilayer functional network constructed from nonstationary multivariate data by analyzing recurrences in application to electroencephalography. Using the recurrence-based index of synchronization, we construct intralayer (within-frequency) and interlayer (cross-frequency) graph edges to model the evolution of a whole-head functional connectivity network during a prolonged stimuli classification task. We demonstrate that the graph edges' weights increase during the experiment and negatively correlate with the response time. We also show that while high-frequency activity evolves toward synchronization of remote local areas, low-frequency connectivity tends to establish large-scale coupling between them.

Predicting Stimulus Modality and Working Memory Load During Visual- and Audiovisual-Acquired Equivalence Learning

Scholars have extensively studied the electroencephalography (EEG) correlates of associative working memory (WM) load. However, the effect of stimulus modality on EEG patterns within this process is less understood. To fill this research gap, the present study re-analyzed EEG datasets recorded during visual and audiovisual equivalence learning tasks from earlier studies. The number of associations required to be maintained (WM load) in WM was increased using the staircase method during the acquisition phase of the tasks. The support vector machine algorithm was employed to predict WM load and stimulus modality using the power, phase connectivity, and cross-frequency coupling (CFC) values obtained during time segments with different WM loads in the visual and audiovisual tasks. A high accuracy (>90%) in predicting stimulus modality based on power spectral density and from the theta-beta CFC was observed. However, accuracy in predicting WM load was higher (≥75% accuracy) than that in predicting stimulus modality (which was at chance level) using theta and alpha phase connectivity. Under low WM load conditions, this connectivity was highest between the frontal and parieto-occipital channels. The results validated our findings from earlier studies that dissociated stimulus modality based on power-spectra and CFC during equivalence learning. Furthermore, the results emphasized the importance of alpha and theta frontoparietal connectivity in WM load.

Patients with fibromyalgia show increased beta connectivity across distant networks and microstates alterations in resting-state electroencephalogram

Fibromyalgia (FM) is a chronic condition characterized by widespread pain of unknown etiology associated with alterations in the central nervous system. Although previous studies demonstrated altered patterns of brain activity during pain processing in patients with FM, alterations in spontaneous brain oscillations, in terms of functional connectivity or microstates, have been barely explored so far. Here we recorded the EEG from 43 patients with FM and 51 healthy controls during open-eyes resting-state. We analyzed the functional connectivity between different brain networks computing the phase lag index after group Independent Component Analysis, and also performed an EEG microstates analysis. Patients with FM showed increased beta band connectivity between different brain networks and alterations in some microstates parameters (specifically lower occurrence and coverage of microstate class C). We speculate that the observed alterations in spontaneous EEG may suggest the dominance of endogenous top-down influences; this could be related to limited processing of novel external events and the deterioration of flexible behavior and cognitive control frequently reported for FM. These findings provide the first evidence of alterations in long-distance phase connectivity and microstate indices at rest, and represent progress towards the understanding of the pathophysiology of fibromyalgia and the identification of novel biomarkers for its diagnosis.

Frontoparietal connectivity correlates with working memory performance in multiple sclerosis

Working Memory (WM) impairment is the most common cognitive deficit of patients with Multiple Sclerosis (MS). However, evidence of its neurobiological mechanisms is scarce. Here we recorded electroencephalographic activity of twenty patients with relapsing-remitting MS and minimal cognitive deficit, and 20 healthy control (HC) subjects while they solved a WM task. In spite of similar performance, the HC group demonstrated both a correlation between temporoparietal theta activity and memory load, and a correlation between medial frontal theta activity and successful memory performances. MS patients did not show theses correlations leading significant differences between groups. Moreover, cortical connectivity analyses using granger causality and phase-amplitude coupling between theta and gamma revealed that HC group, but not MS group, presented a load-modulated progression of the frontal-to-parietal connectivity. This connectivity correlated with working memory capacity in MS groups. This early alterations in the oscillatory dynamics underlaying working memory could be useful for plan therapeutic interventions.

White matter injury and neurodevelopmental disabilities: A cross-disease (dis)connection

White matter (WM) injury, once known primarily in preterm newborns, is emerging in its non-focal (diffused), non-necrotic form as a critical component of subtle brain injuries in many early-life diseases like prematurity, intrauterine growth restriction, congenital heart defects, and hypoxic-ischemic encephalopathy. While advances in medical techniques have reduced the number of severe outcomes, the incidence of tardive impairments in complex cognitive functions or psychopathology remains high, with lifelong detrimental effects. The importance of WM in coordinating neuronal assemblies firing and neural groups synchronizing within multiple frequency bands through myelination, even mild alterations in WM structure, may interfere with the cognitive performance that increasing social and learning demands would exploit tardively during children growth. This phenomenon may contribute to explaining longitudinally the high incidence of late-appearing impairments that affect children with a history of perinatal insults. Furthermore, WM abnormalities have been highlighted in several neuropsychiatric disorders, such as autism and schizophrenia. In this review, we gather and organize evidence on how diffused WM injuries contribute to neurodevelopmental disorders through different perinatal diseases and insults. An insight into a possible common, cross-disease, mechanism, neuroimaging and monitoring, biomarkers, and neuroprotective strategies will also be presented.

Spectral and Anatomical Patterns of Large-Scale Synchronization Predict Human Attentional Capacity

The capacity of visual attention determines how many visual objects may be perceived at any moment. This capacity can be investigated with multiple object tracking (MOT) tasks, which have shown that it varies greatly between individuals. The neuronal mechanisms underlying capacity limits have remained poorly understood. Phase synchronization of cortical oscillations coordinates neuronal communication within the fronto-parietal attention network and between the visual regions during endogenous visual attention. We tested a hypothesis that attentional capacity is predicted by the strength of pretarget synchronization within attention-related cortical regions. We recorded cortical activity with magneto- and electroencephalography (M/EEG) while measuring attentional capacity with MOT tasks and identified large-scale synchronized networks from source-reconstructed M/EEG data. Individual attentional capacity was correlated with load-dependent strengthening of theta (3–8 Hz), alpha (8–10 Hz), and gamma-band (30–120 Hz) synchronization that connected the visual cortex with posterior parietal and prefrontal cortices. Individual memory capacity was also preceded by crossfrequency phase–phase and phase–amplitude coupling of alpha oscillation phase with beta and gamma oscillations. Our results show that good attentional capacity is preceded by efficient dynamic functional coupling and decoupling within brain regions and across frequencies, which may enable efficient communication and routing of information between sensory and attentional systems.

Cross-frequency phase coupling of brain oscillations and relevance attribution as saliency detection in abstract reasoning

reasoning is associated with the ability to detect relations among objects, ideas, events. It underlies the understanding of other individuals' thoughts and intentions. In natural settings, individuals have to infer relevant associations that have proven to be reliable or precise predictors. Salience theory suggests that the attribution of meaning to stimulus depends on their contingency, saliency, and relevance to adaptation. So far, subjective estimates of relevance have mostly been explored in motivation and implicit learning. Mechanisms underlying formation of associations in abstract thinking with regard to their subjective relevance, or salience, are not clear. Applying novel computational methods, we investigated relevance detection in categorization tasks in 17 healthy individuals. Two models of relevance detection were developed: a conventional one with nouns from the same semantic category, an aberrant one based on an insignificant common feature. Control condition introduced non-related words. The participants were to detect either a relevant principle or an insignificant feature to group presented words. In control condition they inferred that the stimuli were irrelevant to any grouping idea. Cross-frequency phase coupling analysis revealed statistically distinct patterns of synchronization representing search and decision in the models of normal and aberrant relevance detection. Significantly distinct frontotemporal functional networks with central and parietal components in the theta and alpha frequency bands may reflect differences in relevance detection.

Use of complex visual stimuli allows controlled recruitment of cortical networks in infants

OBJECTIVE

To characterize cortical networks activated by patterned visual stimuli in infants, and to evaluate their potential for assessment of visual processing and their associations with neurocognitive development.

METHODS

Three visual stimuli, orientation reversal (OR), global form (GF), and global motion (GM), were presented to cohort of five-month-old infants (N = 26). Eye tracker was used to guide the stimulation and to choose epochs for analysis. Visual responses were recorded with electroencephalography and analysed in source space using weighted phase lag index as the connectivity measure. The networks were quantified using several metrics that were compared between stimuli and correlated to cognitive outcomes.

RESULTS

Responses to OR/GF/GM stimuli were observed in nearly all (96/100/100%) recordings. All stimuli recruited cortical networks that were partly condition-specific in their characteristics. The more complex GF and GM conditions recruited wider global networks than OR. Additionally, strength of the GF network showed positive association with later cognitive performance.

CONCLUSIONS

Network analysis suggests that visual stimulation recruits large-scale cortical networks that extend far beyond the conventional visual streams and that differ between stimulation conditions.

SIGNIFICANCE

The method allows controlled recruitment of wide cortical networks, which holds promise for the early assessment of visual processing and its related higher-order cognitive processes.

Genuine cross-frequency coupling networks in human resting-state electrophysiological recordings

Phase synchronization of neuronal oscillations in specific frequency bands coordinates anatomically distributed neuronal processing and communication. Typically, oscillations and synchronization take place concurrently in many distinct frequencies, which serve separate computational roles in cognitive functions. While within-frequency phase synchronization has been studied extensively, less is known about the mechanisms that govern neuronal processing distributed across frequencies and brain regions. Such integration of processing between frequencies could be achieved via cross-frequency coupling (CFC), either by phase–amplitude coupling (PAC) or by n:m-cross–frequency phase synchrony (CFS). So far, studies have mostly focused on local CFC in individual brain regions, whereas the presence and functional organization of CFC between brain areas have remained largely unknown. We posit that interareal CFC may be essential for large-scale coordination of neuronal activity and investigate here whether genuine CFC networks are present in human resting-state (RS) brain activity. To assess the functional organization of CFC networks, we identified brain-wide CFC networks at mesoscale resolution from stereoelectroencephalography (SEEG) and at macroscale resolution from source-reconstructed magnetoencephalography (MEG) data. We developed a novel, to our knowledge, graph-theoretical method to distinguish genuine CFC from spurious CFC that may arise from nonsinusoidal signals ubiquitous in neuronal activity. We show that genuine interareal CFC is present in human RS activity in both SEEG and MEG data. Both CFS and PAC networks coupled theta and alpha oscillations with higher frequencies in large-scale networks connecting anterior and posterior brain regions. CFS and PAC networks had distinct spectral patterns and opposing distribution of low- and high-frequency network hubs, implying that they constitute distinct CFC mechanisms. The strength of CFS networks was also predictive of cognitive performance in a separate neuropsychological assessment. In conclusion, these results provide evidence for interareal CFS and PAC being 2 distinct mechanisms for coupling oscillations across frequencies in large-scale brain networks.

Genuine interareal cross-frequency coupling (CFC) can be identified from human resting state activity using magnetoencephalography, stereoelectroencephalography, and novel network approaches. CFC couples slow theta and alpha oscillations to faster oscillations across brain regions.

Nonlinear interaction decomposition (NID): A method for separation of cross-frequency coupled sources in human brain

Cross-frequency coupling (CFC) between neuronal oscillations reflects an integration of spatially and spectrally distributed information in the brain. Here, we propose a novel framework for detecting such interactions in Magneto- and Electroencephalography (MEG/EEG), which we refer to as Nonlinear Interaction Decomposition (NID). In contrast to all previous methods for separation of cross-frequency (CF) sources in the brain, we propose that the extraction of nonlinearly interacting oscillations can be based on the statistical properties of their linear mixtures. The main idea of NID is that nonlinearly coupled brain oscillations can be mixed in such a way that the resulting linear mixture has a non-Gaussian distribution. We evaluate this argument analytically for amplitude-modulated narrow-band oscillations which are either phase-phase or amplitude-amplitude CF coupled. We validated NID extensively with simulated EEG obtained with realistic head modelling. The method extracted nonlinearly interacting components reliably even at SNRs as small as -15 dB. Additionally, we applied NID to the resting-state EEG of 81 subjects to characterize CF phase-phase coupling between alpha and beta oscillations. The extracted sources were located in temporal, parietal and frontal areas, demonstrating the existence of diverse local and distant nonlinear interactions in resting-state EEG data. All codes are available publicly via GitHub.

From thoughtless awareness to effortful cognition: alpha - theta cross-frequency dynamics in experienced meditators during meditation, rest and arithmetic

Neural activity is known to oscillate within discrete frequency bands and the synchronization between these rhythms is hypothesized to underlie information integration in the brain. Since strict synchronization is only possible for harmonic frequencies, a recent theory proposes that the interaction between different brain rhythms is facilitated by transient harmonic frequency arrangements. In this line, it has been recently shown that the transient occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms (i.e. f_alpha≈ 12 Hz; f_theta≈ 6 Hz) is enhanced during effortful cognition. In this study, we tested whether achieving a state of ‘mental emptiness’ during meditation is accompanied by a relative decrease in the occurrence of 2:1 harmonic cross-frequency relationships between alpha and theta rhythms. Continuous EEG recordings (19 electrodes) were obtained from 43 highly experienced meditators during meditation practice, rest and an arithmetic task. We show that the occurrence of transient alpha:theta 2:1 harmonic relationships increased linearly from a meditative to an active cognitive processing state (i.e. meditation < rest < arithmetic task). It is argued that transient EEG cross-frequency arrangements that prevent alpha:theta cross-frequency coupling could facilitate the experience of ‘mental emptiness’ by avoiding the interaction between the memory and executive components of cognition.

Music Training, Working Memory, and Neural Oscillations: A Review

This review focuses on reports that link music training to working memory and neural oscillations. Music training is increasingly associated with improvement in working memory, which is strongly related to both localized and distributed patterns of neural oscillations. Importantly, there is a small but growing number of reports of relationships between music training, working memory, and neural oscillations in adults. Taken together, these studies make important contributions to our understanding of the neural mechanisms that support effects of music training on behavioral measures of executive functions. In addition, they reveal gaps in our knowledge that hold promise for further investigation. The current review is divided into the main sections that follow: (1) discussion of behavioral measures of working memory, and effects of music training on working memory in adults; (2) relationships between music training and neural oscillations during temporal stages of working memory; (3) relationships between music training and working memory in children; (4) relationships between music training and working memory in older adults; and (5) effects of entrainment of neural oscillations on cognitive processing. We conclude that the study of neural oscillations is proving useful in elucidating the neural mechanisms of relationships between music training and the temporal stages of working memory. Moreover, a lifespan approach to these studies will likely reveal strategies to improve and maintain executive function during development and aging.

Intrinsic connectome organization across temporal scales: New insights from cross-modal approaches

The discovery of a stable, whole-brain functional connectivity organization that is largely independent of external events has drastically extended our view of human brain function. However, this discovery has been primarily based on functional magnetic resonance imaging (fMRI). The role of this whole-brain organization in fast oscillation-based connectivity as measured, for example, by electroencephalography (EEG) and magnetoencephalography (MEG) is only beginning to emerge. Here, we review studies of intrinsic connectivity and its whole-brain organization in EEG, MEG, and intracranial electrophysiology with a particular focus on direct comparisons to connectome studies in fMRI. Synthesizing this literature, we conclude that irrespective of temporal scale over four orders of magnitude, intrinsic neurophysiological connectivity shows spatial similarity to the connectivity organization commonly observed in fMRI. A shared structural connectivity basis and cross-frequency coupling are possible mechanisms contributing to this similarity. Acknowledging that a stable whole-brain organization governs long-range coupling across all timescales of neural processing motivates researchers to take "baseline" intrinsic connectivity into account when investigating brain-behavior associations, and further encourages more widespread exploration of functional connectomics approaches beyond fMRI by using EEG and MEG modalities.

Neuronal correlates of full and partial visual conscious perception

Stimuli may induce only partial consciousness-an intermediate between null and full consciousness-where the presence but not identity of an object can be reported. The differences in the neuronal basis of full and partial consciousness are poorly understood. We investigated if evoked and oscillatory activity could dissociate full from partial conscious perception. We recorded human cortical activity with magnetoencephalography (MEG) during a visual perception task in which stimulus could be either partially or fully perceived. Partial consciousness was associated with an early increase in evoked activity and theta/low-alpha-band oscillations while full consciousness was also associated with late evoked activity and beta-band oscillations. Full from partial consciousness was dissociated by stronger evoked activity and late increase in theta oscillations that were localized to higher-order visual regions and posterior parietal and prefrontal cortices. Our results reveal both evoked activity and theta oscillations dissociate partial and full consciousness.

An Integrative Model of Effortful Control

This article presents an integrative model of effortful control, a resource-limited top-down control mechanism involved in mental tasks and physical exercises. Based on recent findings in the fields of neuroscience, social psychology and cognitive psychology, this model posits the intrinsic costs related to a weakening of the connectivity of neural networks underpinning effortful control as the main cause of mental fatigue in long and high-demanding tasks. In this framework, effort reflects three different inter-related aspects of the same construct. First, effort is a mechanism comprising a limited number of interconnected processing units that integrate information regarding the task constraints and subject’s state. Second, effort is the main output of this mechanism, namely, the effort signal that modulates neuronal activity in brain regions involved in the current task to select pertinent information. Third, effort is a feeling that emerges in awareness during effortful tasks and reflects the costs associated with goal-directed behavior. Finally, the model opens new avenues for research investigating effortful control at the behavioral and neurophysiological levels.

Coupling and Decoupling between Brain and Body Oscillations

Cross frequency coupling is used to study the cross talk between brain oscillations. In this paper we focus on a special type of frequency coupling between brain and body oscillations, which is reflected by the numerical ratio (r) between two frequencies (m and n; n > m). This approach is motivated by theoretical considerations, indicating that during alert wakefulness brain-body oscillations form a coupled hierarchy of frequencies with integer relationships that are binary multiples (r = n:m = 1:2, 1:4, 1:8…..). During sleep we expect an irrational relationship (r = n/m = irrational number) between brain and body oscillations that reflects decoupling. We analyzed alpha frequency, heart rate, breathing frequency during performance of a memory tasks and in addition spindle frequency from data collected by the SIESTA sleep research group. As predicted, our results show a binary multiple frequency relationship between alpha, heart rate and breathing frequency during task performance but an irrational relationship between spindle frequency, heart rate and breathing frequency during sleep.

Brain Functional Connectivity Through Phase Coupling of Neuronal Oscillations: A Perspective From Magnetoencephalography

Magnetoencephalography has gained an increasing importance in systems neuroscience thanks to the possibility it offers of unraveling brain networks at time-scales relevant to behavior, i.e., frequencies in the 1-100 Hz range, with sufficient spatial resolution. In the first part of this review, we describe, in a unified mathematical framework, a large set of metrics used to estimate MEG functional connectivity at the same or at different frequencies. The different metrics are presented according to their characteristics: same-frequency or cross-frequency, univariate or multivariate, directed or undirected. We focus on phase coupling metrics given that phase coupling of neuronal oscillations is a putative mechanism for inter-areal communication, and that MEG is an ideal tool to non-invasively detect such coupling. In the second part of this review, we present examples of the use of specific phase methods on real MEG data in the context of resting state, visuospatial attention and working memory. Overall, the results of the studies provide evidence for frequency specific and/or cross-frequency brain circuits which partially overlap with brain networks as identified by hemodynamic-based imaging techniques, such as functional Magnetic Resonance (fMRI). Additionally, the relation of these functional brain circuits to anatomy and to behavior highlights the usefulness of MEG phase coupling in systems neuroscience studies. In conclusion, we believe that the field of MEG functional connectivity has made substantial steps forward in the recent years and is now ready for bringing the study of brain networks to a more mechanistic understanding.

Tracking Transient Changes in the Neural Frequency Architecture: Harmonic Relationships between Theta and Alpha Peaks Facilitate Cognitive Performance

The synchronization between neural oscillations at different frequencies has been proposed as a core mechanism for the coordination and integration of neural systems at different spatiotemporal scales. Because neural oscillations of different frequencies can only fully synchronize when their "peak" frequencies form harmonic relationships (e.g., f ₂ = f ₁/2), the present study explored whether the transient occurrence of harmonic cross-frequency relationship between task-relevant rhythms underlies efficient cognitive processing. Continuous EEG recordings (51 human participants; 14 males) were obtained during an arithmetic task, rest and breath focus. In two separate experiments, we consistently show that the proportion of epochs displaying a 2:1 harmonic relationship between alpha (8-14 Hz) and theta (4-8 Hz) peak frequencies (i.e., alpha_peak ≈ 10.6 Hz; theta_peak ≈ 5.3 Hz), was significantly higher when cognitive demands increased. In addition, a higher incidence of 2:1 harmonic cross-frequency relationships was significantly associated with increased alpha-theta phase synchrony and improved arithmetic task performance, thereby underlining the functional relevance of this cross-frequency configuration. Notably, opposite dynamics were identified for a specific range of "nonharmonic" alpha-theta cross-frequency relationships (i.e., alpha_peak/theta_peak = 1.1-1.6), which showed a higher incidence during rest compared with the arithmetic task. The observation that alpha and theta rhythms shifted into harmonic versus nonharmonic cross-frequency relationships depending on (cognitive) task demands is in line with the notion that the neural frequency architecture entails optimal frequency arrangements to facilitate cross-frequency "coupling" and "decoupling".SIGNIFICANCE STATEMENT Neural activity is known to oscillate within discrete frequency bands and the interplay between these brain rhythms is hypothesized to underlie cognitive functions. A recent theory posits that shifts in the peak frequencies of oscillatory rhythms form the principal mechanism by which cross-frequency coupling and decoupling is implemented in the brain. In line with this notion, we show that the occurrence of a cross-frequency arrangement that mathematically enables coupling between alpha and theta rhythms is more prominent during active cognitive processing (compared with rest and non-cognitively demanding tasks) and is associated with improved cognitive performance. Together, our results open new vistas for future research on cross-frequency dynamics in the brain and their functional role in cognitive processing.

Electrophysiological Brain Connectivity: Theory and Implementation

We review the theory and algorithms of electrophysiological brain connectivity analysis. This tutorial is aimed at providing an introduction to brain functional connectivity from electrophysiological signals, including electroencephalography (EEG), magnetoencephalography (MEG), electrocorticography (ECoG), stereoelectroencephalography (SEEG). Various connectivity estimators are discussed, and algorithms introduced. Important issues for estimating and mapping brain functional connectivity with electrophysiology are discussed.

Visual short-term memory through the lifespan: Preserved benefits of context and metacognition

Visual short-term memory (VSTM) ability falls throughout the life span in healthy adults. Using a continuous report task, in a large, population-based sample, we first confirmed that this decline affects the quality and quantity of reported memories as well as knowledge of which item went where. Visual and sensorimotor precision also worsened with advancing age, but this did not account for the reduced memory performance. We then considered two strategies that older individuals might be able to adopt, to offset these memory declines: the use of contextual encoding, and metacognitive monitoring of performance. Context and metacognitive awareness were both associated with significantly better performance, however these effects did not interact with age in our sample. This suggests that older adults retain their capacity to boost memory performance through attention to external context and monitoring of their performance. Strategies that focus on taking advantage of these preserved abilities may therefore help to maintain VSTM performance with advancing age. The article reports on analysis of the Cambridge Centre for Ageing and Neuroscience (Cam-CAN) data. (PsycINFO Database Record

Generalized Cross-Frequency Decomposition: A Method for the Extraction of Neuronal Components Coupled at Different Frequencies

Perceptual, motor and cognitive processes are based on rich interactions between remote regions in the human brain. Such interactions can be carried out through phase synchronization of oscillatory signals. Neuronal synchronization has been primarily studied within the same frequency range, e.g., within alpha or beta frequency bands. Yet, recent research shows that neuronal populations can also demonstrate phase synchronization between different frequency ranges. An extraction of such cross-frequency interactions in EEG/MEG recordings remains, however, methodologically challenging. Here we present a new method for the robust extraction of cross-frequency phase-to-phase synchronized components. Generalized Cross-Frequency Decomposition (GCFD) reconstructs the time courses of synchronized neuronal components, their spatial filters and patterns. Our method extends the previous state of the art, Cross-Frequency Decomposition (CFD), to the whole range of frequencies: it works for any f₁ and f₂ whenever f₁:f₂ is a rational number. GCFD gives a compact description of non-linearly interacting neuronal sources on the basis of their cross-frequency phase coupling. We successfully validated the new method in simulations and tested it with real EEG recordings including resting state data and steady state visually evoked potentials (SSVEP).

The frequency architecture of brain and brain body oscillations: an analysis

Research on brain oscillations has brought up a picture of coupled oscillators. Some of the most important questions that will be analyzed are, how many frequencies are there, what are the coupling principles, what their functional meaning is, and whether body oscillations follow similar coupling principles. It is argued that physiologically, two basic coupling principles govern brain as well as body oscillations: (i) amplitude (envelope) modulation between any frequencies m and n, where the phase of the slower frequency m modulates the envelope of the faster frequency n, and (ii) phase coupling between m and n, where the frequency of n is a harmonic multiple of m. An analysis of the center frequency of traditional frequency bands and their coupling principles suggest a binary hierarchy of frequencies. This principle leads to the foundation of the binary hierarchy brain body oscillation theory. Its central hypotheses are that the frequencies of body oscillations can be predicted from brain oscillations and that brain and body oscillations are aligned to each other. The empirical evaluation of the predicted frequencies for body oscillations is discussed on the basis of findings for heart rate, heart rate variability, breathing frequencies, fluctuations in the BOLD signal, and other body oscillations. The conclusion is that brain and many body oscillations can be described by a single system, where the cross talk - reflecting communication - within and between brain and body oscillations is governed by m : n phase to envelope and phase to phase coupling.

Serial representation of items during working memory maintenance at letter-selective cortical sites

A key component of working memory is the ability to remember multiple items simultaneously. To understand how the human brain maintains multiple items in memory, we examined direct brain recordings of neural oscillations from neurosurgical patients as they performed a working memory task. We analyzed the data to identify the neural representations of individual memory items by identifying recording sites with broadband gamma activity that varied according to the identity of the letter a subject viewed. Next, we tested a previously proposed model of working memory, which had hypothesized that the neural representations of individual memory items sequentially occurred at different phases of the theta/alpha cycle. Consistent with this model, the phase of the theta/alpha oscillation when stimulus-related gamma activity occurred during maintenance reflected the order of list presentation. These results suggest that working memory is organized by a cortical phase code coordinated by coupled theta/alpha and gamma oscillations and, more broadly, provide support for the serial representation of items in working memory.

Neural Activity Is Dynamically Modulated by Memory Load During the Maintenance of Spatial Objects

Visuospatial working memory (WM) is a fundamental but severely limited ability to temporarily remember selected stimuli. Several studies have investigated the underlying neural mechanisms of maintaining various visuospatial stimuli simultaneously (i.e., WM load, the number of representations that need to be maintained in WM). However, two confounding factors, namely verbal representation and encoding load (the number of items that need to be encoded into WM), have not been well controlled in previous studies. In this study, we developed a novel delayed-match-to-sample task (DMST) controlling for these two confounding factors and recorded scalp EEG signals during the task. We found that behavioral performance deteriorated severely as memory load increased. Neural activity was modulated by WM load in a dynamic manner. Specifically, higher memory load induced stronger amplitude in occipital and central channel-clusters during the early delay period, while the inverse trend was observed in central and frontal channel-clusters during late delay. In addition, the same inverse memory load effect, that was lower memory load induced stronger amplitude, was observed in occipital channel-cluster alpha power during late delay. Finally, significant correlations between neural activity and individual reaction time showed a role of late-delay central and frontal channel-cluster amplitude in predicting behavioral performance. Because the occipital cortex is important for visual information maintenance, the decrease in alpha oscillation was consistent with the cognitive role that is “gating by inhibition.” Together, our results from a well-controlled DMST suggest that WM load not exerted constant but dynamic effect on neural activity during maintenance of visuospatial objects.

Interactions between frontal and posterior oscillatory dynamics support adjustment of stimulus processing during reinforcement learning

Reinforcement learning (RL) in humans is subserved by a network of striatal and frontal brain areas. The electrophysiological signatures of feedback evaluation are increasingly well understood, but how those signatures relate to the use of feedback to guide subsequent behavioral adjustment remains unclear. One mechanism for post-feedback behavioral optimization is the modulation of sensory processing. We used source-reconstructed MEG to test whether feedback affects the interactions between sources of oscillatory activity in the learning network and task-relevant stimulus-processing areas. Participants performed a probabilistic RL task in which they learned associations between colored faces and response buttons using trial-and-error feedback. Delta-band (2-4 Hz) and theta-band (4-8 Hz) power in multiple frontal regions were sensitive to feedback valence. Low and high beta-band power (12-20 and 20-30 Hz) in occipital, parietal, and temporal regions differentiated between color and face information. Consistent with our hypothesis, single-trial power-power correlations between frontal and posterior-sensory areas were modulated by the interaction between feedback valence and the relevant stimulus characteristic (color versus identity). These results suggest that long-range oscillatory coupling supports post-feedback updating of stimulus processing.